Neural networks and blood cell identification

The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.     

The path probability method: An atomistic technique of diffusion

The path probability method (PPM) of irreversible statistical mechanics has been successfully applied to various diffusion problems. The major advantage of this atomistic treatment over the phenomenological approach of irreversible thermodynamics is that all Onsager matrix coefficients can be derived analytically so that relations among measurable quantities can be clearly understood in terms of microscopic parameters. This review article attempts to present the PPM in the simplest possible form. The importance of the PPM as an atomistic technique is illustrated using a simple example. The applicability and limitations of the technique are also emphasized.     

Reversible parotid enlargement and pseudo-Sj?gren's syndrome secondary to hypertriglyceridemia

We describe 2 patients with a Sj?gren-like syndrome apparently secondary to hypertriglyceridemia. Both had bilateral parotitis in addition to musculoskeletal and sicca symptoms. Parotid gland histology revealed fatty infiltration with no inflammation. Therapy with dietary modification and triglyceride lowering drugs resulted in resolution of symptoms and parotid swelling in one patient. In the 2nd patient, hypertriglyceridemia was resistant to triglyceride lowering drugs, and parotid symptoms and swelling continued unabated. Our findings suggest aggressive treatment of hyperlipidemia in pseudo-Sj?gren's syndrome may result in amelioration of musculoskeletal and parotid symptoms.     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

     

The correlations between structural and optical properties of magnetite nanoparticles synthesised from natural iron sand

The correlations between structural and optical properties of magnetite (Fe₃O₄) nanoparticles were analyzed by using X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) Spectroscopy. The magnetite was synthesised from the iron sand using NaOH, HCl, and NaCl as a solvent. From the quantitative analysis of XRD spectra, the crystallite size (D), strain ($\epsilon$), stress ($\sigma$), energy deformation (u), porosity, and specific surface area were determined. The Kramers Kronig (K–K) relations was applied to the FTIR spectra to determine the optical properties: refractive index (n), extinction coefficient (k), the complex dielectric function ($\mathit{\epsilon }$), and the energy loss function (Im(-1/ε)). The uniform strain was produced from the NaOH and HCl as the solvent. The magnetite had higher porosity with NaCl as a solvent and was directly proportional to the optical phonon vibration. We found a perfect correlation; when the difference between two optical phonon increases, the crystallite size decreases, and the strain will be uniform.     

Characterisation and catalytic properties of MCM-41 and Pd/MCM-41 materials

Pure silica MCM-41 mesoporous molecular sieve material was synthesised and characterised by in situ synchrotron XRD, TEM, TGA/DTA and DRIFTS techniques. In situ energy dispersive XRD (EDXRD) confirmed the exact nature of the pore diameter of MCM-41 and the change in crystal structure on calcination. The IR band at 1057 cm^-1 of as-synthesised MCM-41 was shifted by 14 cm^-1 on heating to 673 K due to increased condensation of silanol groups to form Si-O-Si bridges. Calcined MCM-41 materials were used to support Pd, and the catalytic activities for 1-hexene and benzene selective hydrogenation were investigated. The Pd/MCM-41 catalyst showed high activity in hydrogenation of 1-hexene at an inlet reaction temperature of 298 K, but did not show any activity in hydrogenation for benzene. TEM results for the reduced Pd/MCM-41 catalysts revealed that the average Pd particle size was around 2-2.5 nm and these particles were located in the pores of MCM-41 and showed good distribution. TPR measurements showed that about 70% of palladium oxide (PdO) loading in the calcined catalysts was reduced at sub-ambient temperature. This revised version was published online in July 2006 with corrections to the Cover Date.     

Earthquake onset detection using spectro-ratio on multi-threshold time–frequency sub-band

Automatic onset detection and picking algorithm has been proposed by applying the spectro-ratio on time–frequency sub-band. The proposed algorithm does not need any parameter settings as it will work on data generated by either short or very broad band seismometers. Our algorithm is applied on local events from Cairo region recorded by three stations of the Egyptian National Seismic Network (ENSN). Maximum standard deviation is observed to be 0.113 s of the corresponding manual picks made by analysts.     

High-performance MEMS shutter display with metal-oxide thin-film transistors and optimized MEMS element

Active matrix prestressed microelectromechanical shutter displays enable outstanding optical properties as well as robust operating performance. The microelectromechanical systems (MEMS) shutter elements have been optimized for higher light outcoupling efficiency with lower operation voltage and higher pixel density. The MEMS elements have been co-fabricated with self-aligned metal-oxide thin-film transistors (TFTs). Several optimizations were required to integrate MEMS process without hampering the performance of both elements. The optimized display process requires only seven photolithographic masks with ensuring proper compatibility between MEMS shutter and metal-oxide TFT process.     

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.